This course will provide you will the tools necessary to complete the final components of the analyze phase as well as the improve and control phases of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) process. This course is the final course in the Six Sigma Yellow Belt Specialization. You will learn about relationships from data using correlation and regression as well as the different hypothesis terms in hypothesis testing. This course will provide you with tools and techniques for improvement. You will also understand the importance of a control plan, as well as its key characteristics, for maintaining process improvements. Every module will include readings, discussions, lecture videos, and quizzes to help make sure you understand the material and concepts that are studied.
Our applied curriculum is built around the latest handbook The Certified Six Sigma Handbook (2nd edition) and students will develop /learn the fundamentals of Six Sigma. Registration includes online access to course content, projects, and resources but does not include the companion text The Certified Six Sigma Handbook (2nd edition). The companion text is not required to complete the assignments. However, the text is a recognized handbook used by professionals in the field. Also, it is a highly recommended text for those wishing to move forward in Six Sigma and eventually gain certification from professional agencies such as American Society for Quality (ASQ).

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The capstone project allowed us to practice the knowledge learned from this course to real projects.

ZA

Apr 11, 2019

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Real application to what you have learned so far, excellent

From the lesson

Hypothesis Testing

This the final module that covers the Analysis phase of the DMAIC process. Now that you have collected the data and calculated it you will need to determine how to make a statistical conclusion about your findings. In this module you will learn more about the importance of hypothesis testing, how to correctly do a hypothesis test reading as well as how to avoid errors, and statistical significance.

Taught By

Christina Scherrer, PhD

David Cook, PhD

Assistant Professor of Mechanical Engineering Technology

Gregory Wiles, PhD

Interim Chair and Assistant Professor

Bill Bailey, PhD

Assistant Professor of Industrial Engineering

Transcript

Types of Errors: Examples. Suppose you want to test if your equipment is overfilling your SixSigma-O's cereal past the targeted 14.02 ounces? To set up your hypothesis test, you would have your null hypothesis is that mu is less than or equal to 14.02 ounces. Your alternative hypothesis is that mu is greater than 14.02 ounces. Suppose your machine is actually now filling them at 14.21 ounces. You of course don't know that, which is why you're doing the hypothesis test. If the data you collect causes you to reject your null hypothesis, what type of error if any have you made? Well, since the mean 14.21 is greater than 14.02, that means that the alternative hypothesis is correct. So, a correct decision would be to reject the null hypothesis. Therefore, you have made a correct decision. Now, suppose that your machine was actually just filling them to 14.01 ounces but your data still leads you to reject the null hypothesis. In that case, 14.01 is less than or equal to 14.02, so your hypothesis is correct. Since your data causes you to reject the null hypothesis, you have made a Type I Error, since this is the error where you reject the null hypothesis when it is true. Notice also that since you've rejected the null hypothesis, the only possible outcomes were that you were correct or that you made a Type I error. It is not possible to make a Type II Error, when you have rejected the null hypothesis. Now, suppose you want to test whether the mean length of cable produced by machine has changed from 1.5 meters. Your test becomes your null hypothesis is that the mean is equal to 1.5 versus your alternative hypothesis is that it is not equal to 1.5. Suppose that the mean length of the cable is actually 1.6 meters. And your sample mean causes you to fail to reject the null hypothesis. I'd like you to pause this video for a minute and determine what type of error, if any, you have made. Okay so, we failed to reject the null hypothesis. This means that the only two possibilities are that we failed to reject correctly because the null hypothesis is true or that we make a type II error because the null hypothesis is false. In this case it would be a type II error. The alternative hypothesis is true because the mean is equal to 1.6 rather then 1.5, yet we fail to reject the null hypothesis so we've made a type II error. You'll have more opportunity to practice this in the assessments.

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